The Agricultural Innovation Process: Research and Technology Adoption in a Changing Agricultural Sector

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1 The Agricultural Innovation Process: Research and Technology Adoption in a Changing Agricultural Sector (For the Handbook of Agricultural Economics) David Sunding and David Zilberman David Sunding is Cooperative Extension Specialist, Department of Agricultural and Resource Economics, University of California at Berkeley David Zilberman is Professor, Department of Agricultural and Resource Economics, University of California at Berkeley Revised January, 2000 Abstract: The chapter reviews the generation and adoption of new technologies in the agricultural sector. The first section describes models of induced innovation and experimentation, considers the political economy of public investments in agricultural research, and addresses institutions and public policies for managing innovation activity. The second section reviews the economics of technology adoption in agriculture. Threshold models, diffusion models, and the influence of risk, uncertainty, and dynamic factors on adoption are considered. The section also describes the influence of institutions and government interventions on adoption. The third section outlines future research and policy challenges.

2 Keywords: innovation, diffusion, adoption, technology transfer, intellectual property David Sunding: tel: ; fax: ; David Zilberman: tel: ; fax: ; 2

3 The Agricultural Innovation Process: Research and Technology Adoption in a Changing Agricultural Sector Technological change has been a major factor shaping agriculture in the last 100 years [Schultz (1964); Cochrane (1979)]. A comparison of agricultural production patterns in the United States at the beginning (1920) and end of the century (1995) shows that harvested cropland has declined (from 350 to 320 million acres), the share of the agricultural labor force has decreased substantially (from 26 to 2.6 percent), and the number of people now employed in agriculture has declined (9.5 million in 1920 vs. 3.3 million in 1995); yet agricultural production in 1995 was 3.3 times greater than in 1920 [United States Bureau of the Census (1975, 1980, 1998)]. Internationally, tremendous changes in production patterns have occurred. While world population more than doubled between 1950 and 1998 (from 2.6 to 5.9 billion), grain production per person has increased by about 12 percent, and harvested acreage per person has declined by half [Brown, Gardner, and Halweil (1999)]. These figures suggest that productivity has increased and agricultural production methods have changed significantly. 1

4 There is a large amount of literature investigating changes in productivity, 1 which will not be addressed here. Instead this chapter presents an overview of agricultural economic research on innovations the basic elements of technological and institutional change. Innovations are defined here as new methods, customs, or devices used to perform new tasks. The literature on innovation is diverse and has developed its own vocabulary. We will distinguish between two major research lines: research on innovation generation and research on the adoption and use of innovation. Several categories of innovations have been introduced to differentiate policies or modeling. For example, the distinction between innovations that are embodied in capital goods or products (such as tractors, fertilizers, and seeds) and those that are disembodied (e.g., integrated pest management schemes) is useful for directing public investment in innovation generation. Private parties are less likely to invest in generating disembodied innovations because of the difficulty in selling the final product, so that is an area for public action. Private investment in the generation of embodied innovations requires appropriate institutions for intellectual property rights protection, as we will see below. The classification of innovations according to form is useful for considering policy questions and understanding the forces behind the generation and adoption of innovations. Categories in this classification include mechanical innovations (tractors 1 See Mundlak (1997), Ball et al. (1997), and Antle and McGuckin (1993). 2

5 and combines), biological innovations (new seed varieties), chemical innovations (fertilizers and pesticides), agronomic innovations (new management practices), biotechnological innovations, and informational innovations that rely mainly on computer technologies. Each of these categories may raise different policy questions. For example, mechanical innovations may negatively affect labor and lead to farm consolidation. Chemical and biotechnological innovations are associated with problems of public acceptance and environmental concerns. We will argue later that economic forces as well as the state of scientific knowledge affect the form of innovations that are generated and adopted in various locations. Another categorization of innovation according to form distinguishes between process innovations (e.g., a way to modify a gene in a plant) and product innovations (e.g., a new seed variety). The ownership of rights to a process that is crucial in developing an important product may be a source of significant economic power. We will see how intellectual property rights and regulations affect the evolution of innovation and the distribution of benefits derived from them. Innovations can also be distinguished by their impacts on economic agents and markets which affect their modeling; these categories include yield-increasing, costreducing, quality-enhancing, risk-reducing, environmental-protection increasing, and shelf-life enhancing. Most innovations fall into several of these categories. For example, a new pesticide may increase yield, reduce economic risk, and reduce environmental 3

6 protection. The analysis of adoption or the impact of risk-reducing innovations may require the incorporation of a risk-aversion consideration in the modeling framework, while investigating the economics of a shelf-life enhancing innovation may require a modeling framework that emphasizes inter-seasonal dynamics. Three sections on the generation of innovations follow in Part I. The first introduces results of induced innovation models and the role of economic forces in triggering innovations; the second presents a political-economic framework for government financing of innovations; and the third addresses various institutions and policies for managing innovation activities. Part II discusses the adoption of innovations and includes four sections. The first section considers threshold models and models of diffusion as a process of imitation; the second presents adoption under uncertainty; the third addresses dynamic considerations on adoption; and the last two sections deal with the impact of institutional and policy constraints on adoption. Part III addresses future directions. 4

7 I. GENERATION OF INNOVATION Induced Innovations There are several stages in the generation of innovations. These stages are depicted in Figure 1. The first stage is discovery, characterized by the emergence of a concept or results that establish the innovation. A second essential stage is development, where the discovery moves from the laboratory to the field, and is scaled up, commercialized, and integrated with other elements of the production process. In cases of patentable innovations, between the time of discovery and development there may also be a stage where there is registration for a patent. If the innovation is embodied, once it is developed it has to be produced and, finally, marketed. For embodied innovations, the marketing stage consists of education, demonstration, and sales. Only then does adoption occur. Insert Figure 1 Some may hold the notion that new discoveries are the result of inspiration occurring randomly without a strong link to physical reality. While that may sometimes be the case, Hayami and Ruttan (1985) formalized and empirically verified their theory of induced innovations that closely linked the emergence of innovations with economic conditions. They argued that the search for new innovations is an economic activity that 5

8 is significantly affected by economic conditions. New innovations are more likely to emerge in response to scarcity and economic opportunities. For example, labor shortages will induce labor-saving technologies. Environmental-friendly techniques are likely to be linked to the imposition of strict environmental regulation. Drip irrigation and other water-saving technologies are often developed in locations where water constraints are binding, such as Israel and the California desert. Similarly, food shortages or high prices of agricultural commodities will likely lead to the introduction of a new high-yield variety, and perceived changes in consumer preferences may provide the background for new innovations that modify product quality. The work of Boserup (1965) and Binswanger and McIntire (1987) on the evolution of agricultural systems supports the induced-innovation hypothesis. Early human groups, consisting of a relatively small number of members who could roam large areas of land, were hunters and gatherers. An increase in population led to the evolution of agricultural systems. In tropical regions where population density was still relatively small, farmers relied on slash and burn systems. The transition to more intensive farming systems that used crop rotation and fertilization occurred as population density increased even further. The need to overcome diseases and to improve yields led to the development of innovations in pest control and breeding, and the evolution of the agricultural systems we are familiar with. The work of Berck and Perloff (1985) suggests that the same phenomena may occur with seafood. An increased demand for fish and 6

9 expanded harvesting may lead to the depletion of population and a rise in harvesting costs, and thus trigger economic incentives to develop alternative aquaculture and mariculture for the provision of seafood. While scarcity and economic opportunities represent potential demand that is, in most cases, necessary for the emergence of new innovations, a potential demand is not sufficient for inducing innovations. In addition to demand, the emergence of new innovations requires technical feasibility and new scientific knowledge that will provide the technical base for the new technology. Thus, in many cases, breakthrough knowledge gives rise to new technologies. Finally, the potential demand and the appropriate knowledge base are integrated with the right institutional setup, and together they provide the background for innovation activities. These ideas can be demonstrated by an overview of some of the major waves of innovations that have affected U.S. agriculture in the last 150 years. New innovations currently are linked with discoveries of scientists in universities or firms. However, in the past, practitioners were responsible for most breakthroughs. Over the years, the role of research labs in producing new innovations has drastically increased, but field experience is still very important in inspiring innovations. John Deere, who invented the steel plow, was a farmer. This innovation was one of a series of mechanical innovations that were of crucial importance to the westward expansion of U.S. agriculture in the nineteenth century. At the time, the United States had vast tracts 7

10 of land and a scarcity of people; this situation induced a wide variety of labor-saving innovations such as the thresher, several types of mechanical harvesters, and later the tractor. Olmstead and Rhode (1993) argue that demand considerations represented by the induced-innovation hypothesis do not provide the sole explanation for the introduction of new technologies. They conclude that during the nineteenth century, when farm machinery (e.g., the reaper) was introduced in the United States, land prices increased relative to labor prices, which seems to contradict the induced-innovation hypothesis. As settlement of the West continued and land became more scarce, land prices may have risen relative to labor, but the cost of labor in America relative to other regions was high, and that provided the demand for mechanical innovations. Olmstead and Rhode (1993) argue that other factors also affected the emergence of these innovations, including the expansion of scientific knowledge in metallurgy and mechanics (e.g., the Bessemer process for the production of steel, and the invention of various types of mechanical engines), the establishment of the input manufacturing industry, and the interactive relationship between farmers and machinery producers. The infrastructure that was established for the refinement, development, and marketing of the John Deere plow was later used for a generation of other innovations, and the John Deere Company became the world s leading manufacturer of agricultural mechanical equipment. It was able to establish its own research and development (R&D) 8

11 infrastructure for new mechanical innovations, had enough financial leverage to buy the rights to develop other discoveries, and subsequently took over smaller companies that produced mechanical equipment that complemented its own. This pattern of evolution, where an organization is established to generate fundamental innovations of a certain kind, and then later expands to become a leading industrial manufacturer, is repeated in other situations in and out of agriculture. It seems that during the settlement period of the nineteenth century, most of the emphasis was on mechanical innovation. Cochrane (1979) noted that yield per acre did not change much during the nineteenth century, but the production of U.S. agriculture expanded drastically as the land base expanded. However, Olmstead and Rhode (1993) suggest that even during that period there was heavy emphasis on biological innovation. Throughout the settlement period, farmers and scientists, who were part of research organizations such as the Agricultural Research Service (ARS) of the United States Department of Agriculture (USDA), and the experiment stations at the land-grant universities in the United States, experimented with new breeds, both domestic and imported, and developed new varieties that were compatible with the agro-climatic conditions of the newly settled regions. These efforts maintained per-acre yields. Once most of the arable agricultural land of the continental United States was settled, expansion of agricultural production was feasible mostly through increases in yields per acre. The recognition of this reality and the basic breakthroughs in genetics 9

12 research in the nineteenth century increased support for research institutions in their efforts to generate yield-increasing innovations. Most of the developed countries established agricultural research institutions. After World War II, a network of international research centers was established to provide agricultural innovations for developing countries. The establishment of these institutions reflected the recognition that innovations are products of R&D activities, and that the magnitude of these activities is affected by economic incentives. Economic models have been constructed to explain patterns of investment in R&D activities and the properties of the emerging innovations. Evenson and Kislev (1976) developed a production function of research outcomes particularly appropriate for crop and animal breeding. In breeding activities, researchers experiment with a large number of varieties to find the one with the highest yield. The outcome of research efforts depends on a number of plots. In their model, the yield per acre of a crop is a random variable that can assume numerous values. Each experiment is a sampling of a value of this random variable and, if experiments are conducted, the experiment with the highest value will be chosen. Let Y n be yield per acre of the nth experiment and n assumes value from 1 to N. The outcome of n experiments is Y * N = max{ Y 1,...,Y N }. Y * N is the maximum value of the n experiment. Each Y n can assume the value in the range of 10

13 ( 0,Y X ) with probability density g( Y n ) so that Y max g( Y n )dy n = 1. The outcome of research { Y n }. n =1,N on N plots Y * N is a random variable with the expected value µ ( N) = E max Evenson and Kislev (1976) showed that the expected value of Y N * increases with the number of the experiment, i.e., µ N = EY N > 0, µ NN = 2 EY N 2 < 0. As in Evenson and Kislev, consider the determination of optimal research levels when a policymaker s objective is to maximize net expected gain from research. Assume that the research improves the productivity of growers in a price-taking industry with output price P and acreage L. The new innovation is adopted fully and does not require extra research cost. The optimal research program is determined by solving The first-order condition is max N PL( U( N) ) C(N). PL µ N C N = 0, (1) where C N is the cost of the Nth research plot, and C N > 0, C NN > 0. Condition (1) implies that the optimal number of experiments is such that the expected value of the marginal experiment, ( PLµ N ), equals the marginal cost of experiments, ( C N ). Furthermore, the analysis can show that the research effort increases with the size of the region, ( N L > 0), and the scarcity of the product, ( N * P > 0). Similarly, lower research costs will lead to more research effort. 11

14 The outcome of research leading to innovations is subject to much uncertainty and, in cases where a decision maker is risk averse, risk considerations will affect whether and to what extent experiments will be undertaken. For simplicity, consider a case where decision-makers maximize a linear combination of mean and variance of profits, and thus the optimization problem is max N PL [ µ ( N) C(N) ] 1 2 φp2 L 2 σ 2 (N), where σ 2 (N) is the variance of Y N *, the maximum value of yield of N experiments, and φ is a risk-aversion coefficient. The variance of maximum outcome of N experiments declines with N in most cases so that σ N 2 = σ 2 (N) / N<0. The first-order condition determining N is PLµ N φσ N 2 P 2 L 2 C N = 0. (2) Under risk aversion, N is determined so that the marginal effect of an increase of N or expected revenues plus the marginal reduction in the cost of risk bearing is equal to the marginal cost of experiments. A comparison of conditions (1) and (2) suggests that the risk-reducing effect of extra experiments will increase the marginal benefit of experiments under risk aversion. Thus, a risk-averse decision-maker who manages a line of research, is likely to carry out more experiments than a risk-neutral decision-maker. Note, however, that expected profits under risk aversion are smaller than under risk neutrality since risk-neutral decision-makers do not have a risk-carrying cost. If 12

15 experimentation has a significant fixed cost ( C(N) = C 0 + C 1 (N)), there may be situations when risk aversion may prevent carrying out certain lines of research that would be done under risk neutrality. Furthermore, one can expand the model to show that risk considerations may lead risk-averse decision makers to carry out several substitutable research lines simultaneously in order to diversify and reduce the cost of risk bearing. Thus, uncertainty about the research outcome may deter investment in discovery research, but it may increase and diversify the research efforts once they take place. There has not been much research on investment in certain lines of research over time. However, the Evenson-Kislev model suggests that there is a decreasing expected marginal gain from experiments. If a certain yield was established after an initial period of experimentation, the model can be expanded to show that the greater the initial yield, the smaller the optimal experiment in the second period. That suggests that the number of experiments carried out in a certain line of research will decline over time, especially once significant success is obtained, or when it is apparent that there are decreasing marginal returns to research. On the other hand, technological change that reduces the cost of innovative efforts may increase experimentation. Indeed, we have witnessed, over time, the tendency to move from one research line to another and, thus, both dynamic and risk considerations tend to diversify innovative efforts. 13

16 The Evenson-Kislev model explains optimal investment in one line of research. However, research programs consist of several research lines. The model considers a price-taking firm that produces Y units of output priced at P and also generates its own technology through innovative activities (research and development). There are J parallel lines of innovation, and j is the research line indicator, j = 1, J. Let V j be the price of one unit of the jth innovation line and m j be the number of units used in this line. Innovations affect output through a multiplicative effect to the production function, g( m i,...,m j ), and by improving input use effectiveness. The producers use I input, and i is the input indicator, i =1, I. Let the vector of inputs be m = { m i,..., m j }. We distinguish between the actual unit of input i used by the producer, X i, and the effective input e i where e i = h i ( m)x i. Thus, it is assumed that a major effect of the innovation is to increase input use efficiency, and the function h i ( m) denotes the effect of all the lines of input effectiveness. An innovative line j may increase effectiveness of input i, and in this case h i / m j > 0. Thus, the production function of the producer is Y = g( m) f ( X,h 1 ( m), X 2 h 2 ( m),...,h i ( m) ). For simplicity, assume that, without any investment in innovation, h i ( m) = 1, for all the ith; thus, Y = f ( X 1,..., X 2 ). The producer has to determine optimal allocation of resources among inputs and research lines. In particular, the choice problem is 14

17 max X 1 X m 1, m j [ ] w i pg( m) f X 1 h 1 ( m), X 2 h 2 ( m), X 3 h 3 ( m), X I h I ( m) I X i v i m i i =1 J j =1 max X 1 X m 1, m j [ ] w i pg( m) f X,h 1 ( m), X 2 h 2 ( m), X 3 h 3 ( m)x I h I ( m) I X i v i m i ; i =1 J j = 1 where w i is the price of the ith input and v j is the price of one unit of the jth line of innovation. The first-order condition to determine use of the ith input is pg( m) F h e i ( w) w i = 0 for i = 1, I. (3) i Input i will be chosen at a level where the value of marginal product of input i s effective units, pg( m) F, is equal to the price of input i s effective units, which is w e i / h i ( m). If i the innovations have a positive multiplicative effect, g( m) > 1, and increase input use efficiency, h i ( m) > 1, then the analysis in Khanna and Zilberman (1997) suggests that innovations are likely to increase output but may lead to either an increase or decrease in input use. Input use is likely to increase with the introduction of innovations in cases where they lead to substantial increases in output. Modest output effects of innovations are likely to be associated with reduced input use levels. 2 The optimal effort devoted to innovation line j is determined according to I g h pf ( m) + g( m)p i X m i m i v j = 0. (4) j i=1 15

18 Let the elasticity of the multiplicative effect of innovation with respect to the level of g innovation j be denoted by ε m j = g m j m j, and let the elasticity of input i s g( m) h effectiveness coefficient, with respect to the level of innovation j, be ε i m j = h i m j m j h i. Using (3), the first order condition (4) becomes g PY ε m j I h + S i ε i mj m j v j = 0, (5) i=1 where S i = w i X i / PY is the revenue share of input i. Condition (5) states that, under optimal resource allocation, the expenditure share (in total revenue of innovation line j) will be equal to the sum of elasticities of the input effectiveness, with respect to research line j, and the elasticity of the multiplicative output coefficient with respect to this research line. This condition suggests that more resources are likely to be allocated to research lines with higher productivity effects that mostly impact inputs with higher expenditure shares that have a relatively lower cost. 3 Risk considerations provide part of the explanation for such diversification, but whether innovations are complements or substitutes may also be a factor. When the tomato harvester was introduced in California, it was accompanied by the introduction of a new complementary tomato variety [de Janvry, LeVeen, and Runsten (1981)]. 2 Khanna and Zilberman (1997) related the impact of technological change on input use to the curvature of the production function. If marginal productivity of e i declines substantially with an increase in e i, the output effects are restricted and innovation leads to reduced input use. 3 Binswanger (1974) proves these assertions under a very narrow set of conditions. 16

19 McGuirk and Mundlak s (1991) analysis of the introduction of high-yield green revolution varieties in the Punjab shows that it was accompanied by the intensification of irrigation and fertilization practices. The induced innovation hypothesis can be expanded to state that investment in innovative activities is affected by shadow prices implied by government policies and regulation. The tomato harvester was introduced following the end of the Bracero Program, whose termination resulted in reduced availability of cheap immigrant workers for California and Florida growers. Environmental concerns and regulation have led to more intensive research and alternatives for the widespread use of chemical pesticides. For example, they have contributed to the emergence of integrated pest management strategies and have prompted investment in biological control and biotechnology alternatives to chemical pesticides. Models of induced innovation should be expanded to address the spatial variability of agricultural production. The heterogeneity of agriculture and its vulnerability to random events such as changes in weather and pest infestation led to the development of a network of research stations. A large body of agricultural research has been aimed at adaptive innovations that develop practices and varieties that are appropriate for specific environmental and climatic conditions. The random emergence of new diseases and pests led to the establishment of research on productivity 17

20 maintenance aimed at generating new innovations in response to adverse outcomes whenever they occurred. The treatment of the mealybug in the cassava in Africa is a good example of responsive research. Cassava was brought to Africa from South America 300 years ago and became a major subsistence crop. The mealybug, one of the pests of cassava in South America, was introduced to Africa and reduced yields by more than 50 percent in ; without treatment, the damage could have had a devastating effect on West Africa [Norgaard (1988)]. The International Institute of Tropical Agriculture launched a research program which resulted in the introduction of a biological control in the form of a small wasp, E lopezi, that is a natural enemy of the pest in South America. Norgaard estimated the benefit/cost ratio of this research program to be 149 to 1, but his calculation did not take into account the cost of the research that established the methodology of biological control, and the fixed cost associated with maintaining the infrastructure to respond to the problem. Induced innovation models such as Binswanger s (1974) are useful in linking the evolution of innovations to prices, costs, and technology. However, they ignore some of the important details that characterize the system leading to agricultural innovations. 4 Typically, new agricultural technologies are not used by the entities that develop them 4 The Binswanger model (1974) is very closely linked to the literature on quantifying sources of productivity in agriculture. For an overview of this important body of literature, which benefited from seminal contributions by Griliches (1957, 1958) and Mundlak, see Antle and McGuckin (1993). 18

21 (e.g., universities and equipment manufacturers). Different types of entities have their distinct decision-making procedures that need to be recognized in a more refined analysis of agricultural innovations. The next subsection will analyze resource allocation for the development of new innovations in the public sector, and that will be followed by a discussion of specific institutions and incentives for innovation activities (patents and intellectual property rights) in the private sector. Induced innovations by agribusiness apply to innovations beyond the farm gate. In much of the post World War II period, there has been an excess supply of agricultural commodities in world markets. This has led to a period of low profitability in agriculture requiring government support. While increasing food quantity has become less of a priority, increasing the value added to food products has become a major concern of agriculture and agribusiness in developed nations. Indeed, that has been the essence of many of the innovations related to agriculture in the last 30 years. Agribusiness took advantage of improvements in transportation and weather-controlled technologies that led to innovations in packing, storage, and shipping. These changes expanded the availability as well as the quality of meats, fruits, and vegetables; increased the share of processing and handling in the total food budget; and caused significant changes in the structure of both food marketing industries and agriculture. It is important to understand the institutional setup that enables these innovations to materialize. While there has not been research in this area, it seems that the 19

22 availability of numerous sources of funding to finance new ventures (e.g., venture capital, stock markets, mortgage markets, credit lines from buyers) enables the entities that own the rights to new innovations to change the way major food items are produced, marketed, and consumed. Political Economy of Publicly Funded Innovations Applied R&D efforts are supported by both the public and private sectors because of the innovations they are likely to spawn. Public R&D efforts are justified by the public-good nature of these activities and the inability of private companies to capture all the benefits resulting from farm innovations. Studies have found consistently high rates of returns (above 20 percent) to public investment in agricultural research and extension, indicating underinvestment in these activities [see Alston, Norton, and Pardey (1995); Huffman (1998)]. Analysis of patterns of public spending for R&D in agriculture shows that federal monies tend to emphasize research on science and commodities which are grown in several states (e.g., wheat, corn, rice), while individual states provide much of the public support for innovation-inducing activities for crops that are specialties of the state (e.g., tomatoes and citrus in Florida, and fruits and vegetables in California). The process of devolution has also applied to public research and, over the years, the federal share in public research has declined relative to the state s share. Increased concern for environmental and resource 20

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